Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add devkindhq/ideogram-ai-toolkit --skill logo-promptinggit clone --depth 1 https://github.com/devkindhq/ideogram-ai-toolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/devkindhq/ideogram-ai-toolkit/logo-prompting)<a href="https://agentmods.dev/skills/devkindhq/ideogram-ai-toolkit/logo-prompting"><img src="https://agentmods.dev/badge/skills/devkindhq/ideogram-ai-toolkit/logo-prompting/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/devkindhq/ideogram-ai-toolkit/logo-prompting"><img src="https://agentmods.dev/badge/skills/devkindhq/ideogram-ai-toolkit/logo-prompting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00169 | $0.01792 |
| Opus 5.5 | $0.00068 | $0.00717 |
| Sonnet 5.5 | $0.00034 | $0.00358 |
| Haiku 4.5 | $0.00017 | $0.00179 |
Grade A, and why
logo-prompting scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Logo Prompting
Writing a good logo prompt is a compression problem: you're translating a brand's strategy and voice into a short, literal, image-model-legible spec — while actively fighting the model's own priors, which default to AI-startup visual clichés (gradients, neural nodes, glowing orbs, soundwave bars) unless explicitly told not to.
This skill exists because a logo prompt has two failure modes, and they pull in opposite directions:
- Too vague → the model falls back to generic SaaS-logo training-data averages.
- Too literal / over-specified → the model draws exactly the clichéd icon you named (a phone for a calling app, a shield for security, a lightbulb for ideas) instead of an abstract mark that earns its meaning.
Good logo prompting threads between these: specific about constraints (palette, type, mood, what to avoid), abstract about the mark itself (suggest, don't depict).
Before writing a prompt: gather the four inputs
Don't start typing a prompt cold. Pull these four things first — they're the actual inputs, the prompt is just their compressed form:
- Brand truth — read the project's
brand.mdif one exists (Strategy + Voice + Visual layers). If there's nobrand.md, ask for the equivalent: what the brand is, what it's not, the archetype, the core promise. A logo prompt with no brand truth behind it is decoration, not identity. - The Visual layer specifically — Colors (exact hex, named tokens, and their usage rule — which color is primary vs. reserved-for-one-state), Typography (display face + weight + roman-only), Style keywords + reference brands. See
references/brand-visual-layer.md. - Intake context — what is this mark actually for (wordmark, icon-only, favicon, both), what does it need to survive (16px favicon, dark mode, embroidery, print), who reads it (a shift worker at 2am glancing at a phone screen reads differently than a landing-page visitor). See
references/logo-offer-questions.mdfor the fuller intake list — use it as an interview checklist when the user hasn't already supplied this. - The anti-slop guardrails — the specific clichés this brand must never look like (see
references/anti-slop-discipline.md). Every brand has its own version of "don't look like a generic AI startup" — for a fintech it might be "no padlock icon," for a care-work app it's "no clinical cross," etc. Name the negatives explicitly; a model that isn't told what to avoid will reach for it.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/fleetline-four-directions.md 6.8 KB
- examples/images/fleetline-01-wordmark.webp 2.7 KB
- examples/images/fleetline-02-badge-emblem.webp 4.5 KB
- examples/images/fleetline-03-mark-only.webp 2.3 KB
- examples/images/fleetline-04-monospace-identity.webp 1.7 KB
- references/anti-slop-discipline.md 4.4 KB
- references/brand-visual-layer.md 3.3 KB
- references/logo-offer-questions.md 2.7 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 66 lines · 169 tokens per session scan A e05d04085d5f
logo-prompting is a skill published in the GitHub repository devkindhq/ideogram-ai-toolkit (2 stars, last pushed 6d ago), licensed Apache-2.0. It adds 169 tokens to every session and 1,792 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
appllama-usage
Use the Appllama MCP (mcp.appllama.io) well — research real top-grossing mobile apps, their screens, flows, and UI elements, then build from what you learn. Load when the Appllama MCP is connected and the task involves building a mobile app or screen, researching app design patterns, studying…
appllama-app-design-skill
Build native-feeling, benchmark-quality mobile app screens (Expo / React Native). Use when designing or implementing any mobile UI — screens, flows, onboarding, paywalls, tab bars, sheets, settings, empty states — or when polishing motion, navigation, typography, dark mode, or perceived performance. Enforces Apple HIG…
excalidraw
Use when user requests diagrams, flowcharts, architecture charts, or visualizations. Also use proactively when explaining systems with 3+ components, complex data flows, or relationships that benefit from visual representation. Generates .excalidraw files and exports to PNG/SVG via Kroki API or locally using…
build-for-good-ux
Audit, build, or refactor frontend UI components, pages, forms, and interactive flows to implement complete UX states (loading, success, error, empty), resilient async degradation, accessible button interactions, error recovery, Fitts's Law mobile touch targets, and choice simplification. Use when creating or…
html-slides
A workflow for creating a presentation as one self-contained HTML file sized for a 1920×1080 screen. It supports several visual styles and can export to PPTX when requested.
visualkan
Generate visual explanations (whiteboard, infographic, presentation, diagram, mindmap, mockup) from any content, via native subscription image generation or an API backend (OpenAI, Gemini, OpenRouter, Grok).